Updated Tweet Descriptors

## [1] "total tweets (English-only)"
## [1] 69197
## [1] "original non-retweets (raw count):"
## [1] 25031
## [1] "retweet counts (raw count):"
## [1] 44166
## [1] "tweets that are replies:"
## [1] 1328
## [1] "tweets that quote (retweet + added own text on top) tweets:"
## [1] 4079

Updated plot (and some specific numbers to reference)

## # A tibble: 6 x 3
##    year tweets accounts
##   <dbl>  <int>    <int>
## 1  2014   2251     1097
## 2  2015   3209     1215
## 3  2016  28254    13109
## 4  2017  10811     5188
## 5  2018  11701     4552
## 6  2019  12971     5592

Alternative to graph above if we want a labeled graph

Updated Yearly Proportion Figure (and some specific numbers to reference)

##                     topic subtotal percent_total num_retweets percent_retweets
## 1              Abstinence      137          0.20           71            51.82
## 2                  Condom      950          1.37          578            60.84
## 3                    PrEP    13164         19.02         6317            47.99
## 4            Circumcision      836          1.21          364            43.54
## 5                     PEP      725          1.05          281            38.76
## 6          Harm Reduction     1249          1.80          804            64.37
## 7              Viral Load     2216          3.20         1349            60.88
## 8  Violence Against Women      310          0.45          177            57.10
## 9                   EMTCT      168          0.24           85            50.60
## 10            Serosorting        5          0.01            1            20.00
##           topic subtotal percent_total num_retweets percent_retweets
## 11 Microbicides      224          0.32          172            76.79
## 12     Sex Work      222          0.32           62            27.93
## 13      Testing     6000          8.67         3494            58.23

Note: 46580 tweets (including retweets) do not have our themes.

Top 30 words from topicless tweets

Not sure if this help us but see if there are terms we can use to refine our word lists.

## Joining, by = "word"
## # A tibble: 10 x 2
##    word           n
##    <chr>      <int>
##  1 hiv         9697
##  2 world       4869
##  3 people      4682
##  4 access      4097
##  5 aids        3925
##  6 women       3843
##  7 hands       3832
##  8 million     3705
##  9 prevention  3560
## 10 close       3441
## # A tibble: 10 x 2
##    word           n
##    <chr>      <int>
##  1 gap         3430
##  2 action      3429
##  3 girls       3373
##  4 services    3137
##  5 infections  2943
##  6 treatment   2929
##  7 infected    2917
##  8 health      2434
##  9 urgent      2393
## 10 campaign    2250
## # A tibble: 10 x 2
##    word        n
##    <chr>   <int>
##  1 efforts  1953
##  2 day      1939
##  3 sexual   1696
##  4 join     1690
##  5 lives    1555
##  6 adults   1519
##  7 support  1410
##  8 ensure   1318
##  9 key      1312
## 10 leading  1193

Updated Plot

## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)

## # A tibble: 6 x 3
## # Groups:   year [6]
##    year percent Topic 
##   <dbl>   <dbl> <chr> 
## 1  2014    2.58 Condom
## 2  2015    1.34 Condom
## 3  2016    1.14 Condom
## 4  2017    1.42 Condom
## 5  2018    2.09 Condom
## 6  2019    0.99 Condom
## # A tibble: 6 x 3
## # Groups:   year [6]
##    year percent Topic
##   <dbl>   <dbl> <chr>
## 1  2014   13.8  PrEP 
## 2  2015   20.2  PrEP 
## 3  2016    5.29 PrEP 
## 4  2017   17.6  PrEP 
## 5  2018   34.1  PrEP 
## 6  2019   37.1  PrEP 
## # A tibble: 6 x 3
## # Groups:   year [6]
##    year percent Topic       
##   <dbl>   <dbl> <chr>       
## 1  2014    2.98 Circumcision
## 2  2015    0.62 Circumcision
## 3  2016    0.86 Circumcision
## 4  2017    0.82 Circumcision
## 5  2018    1.34 Circumcision
## 6  2019    2.01 Circumcision
## # A tibble: 6 x 3
## # Groups:   year [6]
##    year percent Topic     
##   <dbl>   <dbl> <chr>     
## 1  2014    0.18 Abstinence
## 2  2015    0.28 Abstinence
## 3  2016    0.28 Abstinence
## 4  2017    0.26 Abstinence
## 5  2018    0.11 Abstinence
## 6  2019    0.03 Abstinence
## # A tibble: 6 x 3
## # Groups:   year [6]
##    year percent Topic
##   <dbl>   <dbl> <chr>
## 1  2014    0.31 EMTCT
## 2  2015    0.72 EMTCT
## 3  2016    0.25 EMTCT
## 4  2017    0.21 EMTCT
## 5  2018    0.07 EMTCT
## 6  2019    0.27 EMTCT
## # A tibble: 6 x 3
## # Groups:   year [6]
##    year percent Topic
##   <dbl>   <dbl> <chr>
## 1  2014    1.11 PEP  
## 2  2015    0.56 PEP  
## 3  2016    0.17 PEP  
## 4  2017    0.44 PEP  
## 5  2018    2.28 PEP  
## 6  2019    2.46 PEP  
## # A tibble: 6 x 3
## # Groups:   year [6]
##    year percent Topic  
##   <dbl>   <dbl> <chr>  
## 1  2014    8.53 Testing
## 2  2015    8.13 Testing
## 3  2016    6.31 Testing
## 4  2017    7.21 Testing
## 5  2018   11.5  Testing
## 6  2019   12.7  Testing

Top 20 Words/Hashtags That Include “prep”

After adding the space technique, we were worried about excluding “prep”-related hashtags/words.

So I took the subset that did not tagged as a “prep” tweet. Then tallied prep-containing words/hashtags that could help us refine our current ‘prep’ word list

## Joining, by = "word"
## # A tibble: 97 x 2
##    word               n
##    <chr>          <int>
##  1 hivprep           98
##  2 prepworks         70
##  3 prepineurope      53
##  4 prepare           47
##  5 prepex            43
##  6 prepforireland    43
##  7 prep4women        37
##  8 teamprepster      35
##  9 pleaseprepme      21
## 10 prep4love         21
## # … with 87 more rows

Top Content Generators**

**Based on number of original tweet authorship.

## .
##     HIV_Insight Sex_Worker_Hlth         DrMbere   Hlth_Literacy      HIVIreland 
##            3144             484             465             444             396 
##          UNAIDS  EPICBrowardOrg  Health_HIV2030  HopeAndHelpInc    himmoderator 
##             269             262             240             221             183 
##   itech_network       one2oneKE  GMIPartnership   HIVpxresearch   Prison_Health 
##             170             169             162             152             139 
##   ProceedNCTSTA       UNAIDS_AP     AIDSFreeGen      AniShakari     RectalMicro 
##             135             111             110             110             108

Top Tweets Based on Retweet Count

## # A tibble: 20 x 4
##    retweet_count screen_name   date       text                                  
##            <int> <chr>         <date>     <chr>                                 
##  1           907 UNAIDS        2016-12-01 "1.9 million adults became infected w…
##  2           493 accphivprn    2019-12-09 "Prevent HIV, you should #BabyYoda #H…
##  3           468 UN            2016-12-01 "The world must take urgent &amp; imm…
##  4           329 UNAIDS        2016-11-30 "Join the #worldaidsday campaign and …
##  5           320 tMSMWellness  2019-03-12 "Trans guy/trans masc and into cis gu…
##  6           274 MissUniverse  2016-12-01 "Know your status. Get Tested. \n\nPo…
##  7           239 ourladyj      2019-06-19 "When folks don’t know their worth, t…
##  8           229 UN            2016-11-30 "#HIVprevention efforts must increase…
##  9           226 ChildtoChild  2016-12-02 "#childparticipation is crucial for e…
## 10           186 UNAIDS        2016-11-22 "More than 2 million people are infec…
## 11           184 AdoreDelano   2016-10-16 "I heart my baes ❤️ #RedDress #LGBTQC…
## 12           173 philredcross  2016-12-01 "#HandsUp for #HIVPrevention. Let's s…
## 13           161 UN_Women      2017-12-01 "There are 870,000 new HIV infections…
## 14           159 UNAIDS        2017-03-08 "New UNAIDS report shows urgent need …
## 15           152 UNAIDS        2016-10-03 "UNAIDS launches #WorldAIDSDay campai…
## 16           149 UNAIDS        2019-03-11 "#HIV was the leading cause of death …
## 17           145 GHDatState    2016-12-01 "Today is #WorldAIDSDay! Together we …
## 18           145 FirstLadyRwa… 2016-12-01 "Together, striving for zero new #HIV…
## 19           141 UNAIDS        2019-10-01 "#HIV was the leading cause of death …
## 20           126 UNFPA         2018-12-01 "Ensuring young people can access com…

Top Tweets Based on Times Retweeted

NOTE: Follower count is based on their follower count at the time I collected the data (whenever that is)

## `summarise()` ungrouping output (override with `.groups` argument)
## Selecting by retweet_count
## # A tibble: 10 x 6
##    screen_name   retweet_count follower_count  ratio tweet_authorship like_count
##    <chr>                 <int>          <int>  <dbl>            <int>      <int>
##  1 UNAIDS                11239         258322 0.0435              269       9617
##  2 HIV_Insight            1880          17816 0.106              3144       2459
##  3 MichelSidibe           1551          39187 0.0396               47       1983
##  4 UN                      908       12047848 0.0001                5        804
##  5 MissUniverse            705        1022563 0.0007               13       2661
##  6 UNAIDS_AP               687          11923 0.0576              111        652
##  7 HIVpxresearch           499           8778 0.0568              152        750
##  8 accphivprn              493            243 2.03                  1       1390
##  9 AniShakari              470           5188 0.0906              110        757
## 10 HIVIreland              468           3564 0.131               396        447
## # A tibble: 10 x 6
##    screen_name  retweet_count follower_count   ratio tweet_authorship like_count
##    <chr>                <int>          <int>   <dbl>            <int>      <int>
##  1 GMIPartners…           447           2705 1.65e-1              162        437
##  2 tMSMWellness           443            893 4.96e-1                4        642
##  3 UNFPA                  437         214571 2.00e-3                9        552
##  4 kentbuse               408          10892 3.75e-2               69        435
##  5 DrMbere                346            589 5.87e-1              465         71
##  6 UN_Women               323        1792006 2.00e-4                3        281
##  7 frontlineai…           318          23541 1.35e-2               58        344
##  8 philredcross           308         552795 6.00e-4                6        255
##  9 GlobalFund             305         215735 1.40e-3                6        278
## 10 UNESCO                 304        3197214 1.00e-4                5        265

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## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

Note: Map above using 2018 CDC Data. Prevalence Rate.

Their recommended citation: “Centers for Disease Control and Prevention. NCHHSTP AtlasPlus. Updated 2019. https://www.cdc.gov/nchhstp/atlas/index.htm. Accessed on [insert date].”

Top Counties for Tweets & Retweets

## # A tibble: 10 x 3
## # Groups:   total [9]
##    GEOID total county              
##    <chr> <int> <chr>               
##  1 11001    60 District of Columbia
##  2 13121    46 Fulton              
##  3 36061    38 New York            
##  4 36047    25 Kings               
##  5 39035    23 Cuyahoga            
##  6 06075    20 San Francisco       
##  7 48201    19 Harris              
##  8 06037    18 Los Angeles         
##  9 12011    15 Broward             
## 10 17031    15 Cook

Top 20 Hashtags (if case sensitive)

## .
##   HIVprevention             HIV   HIVPrevention            PrEP    WorldAIDSDay 
##           11618           10439            4379            4069            3308 
##   hivprevention Myrighttohealth            AIDS    worldaidsday    WorldAidsDay 
##            1623             507             497             454             449 
##        BabyYoda    MissUniverse    HIVawareness           women         HandsUp 
##             430             428             402             301             284 
##   HIVPREVENTION       Education         WAD2016        UequalsU          DREAMS 
##             255             249             230             223             181

Top 20 Hashtags (NOT case sensitive)

## .
##   hivprevention             hiv    worldaidsday            prep myrighttohealth 
##           17952           10552            4358            4242             584 
##            aids        babyyoda    missuniverse    hivawareness           women 
##             509             430             428             425             314 
##         handsup         condoms       education        uequalsu         wad2016 
##             297             296             285             257             243 
##    circumcision         endaids      hivtesting          dreams       handsupja 
##             197             194             190             181             175

Word Cloud Top 100 Words

## Loading required package: RColorBrewer
## Joining, by = "word"

Word Networks (Using top 50 bigrams or word-pairings).

## # A tibble: 23,351 x 3
##    word1       word2           n
##    <chr>       <chr>       <int>
##  1 exposure    prophylaxis   712
##  2 pre         exposure      647
##  3 hiv         prevention    480
##  4 hiv         testing       340
##  5 ht          mt            229
##  6 prophylaxis prep          159
##  7 inject      drugs         144
##  8 medical     male          134
##  9 mt          ht            130
## 10 hiv         pre           128
## # … with 23,341 more rows